Pairwise efficiency: a new mathematical approach to qPCR data analysis increases the precision of the calibration curve assay

Transcriptome-Based Weighted Gene Co-Expression Network Analysis Reveals the Photosynthesis Pathway and Hub Genes Involved in Promoting Tiller Growth under Repeated Drought-Rewatering Cycles in Perennial Ryegrass

Drought stress, which often occurs repeatedly across the world, can cause multiple and long-term effects on plant growth. However, the repeated drought-rewatering effects on plant growth remain uncertain. This study was conducted to determine the effects of drought-rewatering cycles on aboveground growth and explore the underlying mechanisms. Perennial ryegrass plants were subjected to three watering regimes: well-watered control (W), two cycles of drought-rewatering (D2R), and one cycle of drought-rewatering (D1R). The results indicated that the D2R treatment increased the tiller number by 40.9% and accumulated 28.3% more aboveground biomass compared with W; whereas the D1R treatment reduced the tiller number by 23.9% and biomass by 42.2% compared to the W treatment. A time-course transcriptome analysis was performed using crown tissues obtained from plants under D2R and W treatments at 14, 17, 30, and 33 days (d). A total number of 2272 differentially expressed genes (DEGs) were identified. In addition, an in-depth weighted gene co-expression network analysis (WGCNA) was carried out to investigate the relationship between RNA-seq data and tiller number. The results indicated that DEGs were enriched in photosynthesis-related pathways and were further supported by chlorophyll content measurements. Moreover, tiller-development-related hub genes were identified in the D2R treatment, including F-box/LRR-repeat MAX2 homolog (D3), homeobox-leucine zipper protein HOX12-like (HOX12), and putative laccase-17 (LAC17). The consistency of RNA-seq and qRT-PCR data were validated by high Pearson's correlation coefficients ranging from 0.899 to 0.998. This study can provide a new irrigation management strategy that might increase plant biomass with less water consumption. In addition, candidate photosynthesis and hub genes in regulating tiller growth may provide new insights for drought-resistant breeding.

PCR standard curve quantification in an extensive wastewater surveillance program: results from the Dutch SARS-CoV-2 wastewater surveillance

Since the start of the COVID-19 pandemic in 2020, wastewater surveillance programs were established, or upscaled, in many countries around the world and have proven to be a cost-effective way of monitoring infectious disease pathogens. Many of these programs use RT-qPCR, and quantify the viral concentrations in samples based on standard curves, by including preparations of a reference material with known nucleic acid or virus concentrations in the RT-qPCR analyses. In high-throughput monitoring programs it is possible to combine data from multiple previous runs, circumventing the need for duplication and resulting in decreased costs and prolonged periods during which the reference material is obtained from the same batch. However, over time, systematic shifts in standard curves are likely to occur. This would affect the reliability and usefulness of wastewater surveillance as a whole. We aim to find an optimal combination of standard curve data to compensate for run-to-run measurement variance while ensuring enough flexibility to capture systematic longitudinal shifts. Based on more than 4000 observations obtained with the CDC N1 and N2 assays, taken as a part of the National Sewage Surveillance program at the Dutch National Institute for Public Health and the Environment, we show that seasonal and long-term shifts in RT-qPCR efficiency and sensitivity occur. We find that in our setting, using five days of standard-curve data to quantify, results in the least error prone curve or best approximation. This results in differences up to 100% in quantified viral loads when averaged out over a nationwide program of >300 treatment plants. Results show that combining standard curves from a limited set of runs can be a valid approach to quantification without obscuring the trends in the viral load of interest.

Comparative Performance of Eight PCR Methods to Detect Cryptosporidium Species

Diagnostic approaches based on PCR methods are increasingly used in the field of parasitology, particularly to detect Cryptosporidium. Consequently, many different PCR methods are available, both “in-house” and commercial methods. The aim of this study was to compare the performance of eight PCR methods, four “in-house” and four commercial methods, to detect Cryptosporidium species. On the same DNA extracts, performance was evaluated regarding the limit of detection for both C. parvum and C. hominis specificity and the ability to detect rare species implicated in human infection. Results showed variations in terms of performance. The best performance was observed with the FTD^® Stool parasites method, which detected C. parvum and C. hominis with a limit of detection of 1 and 10 oocysts/gram of stool respectively; all rare species tested were detected (C. cuniculus, C. meleagridis, C. felis, C. chipmunk, and C. ubiquitum), and no cross-reaction was observed. In addition, no cross-reactivity was observed with other enteric pathogens. However, commercial methods were unable to differentiate Cryptosporidium species, and generally, we recommend testing each DNA extract in at least triplicate to optimize the limit of detection.

Quantitative PCR of Small Nucleic Acids: Size Matters

Quantitative dysregulation in small nucleic acids (NA), such as microRNA (miRNA), extracted from minimally invasive biopsies, such as, blood, stool, urine, nose, throat, are promising biomarker for diseases diagnosis and management. We quantify the effect of the extra step of poly(A) ligation for cDNA synthesis and small size of the NA on the limit of quantification (LOQ) of quantitative PCR (qPCR), the gold standard to measure copy number. It was discovered that for small NA, the cycle threshold, Ct that is proportional to -log[c], where [c] is the concentration of the target NA exhibits a sharp transition. The results indicate that although the limit of detection (LOD) of qPCR can be in femtomolar range, the LOQ is significantly reduced by well over three orders of magnitude, in picomolar range. Specifically, the study reveals that the PCR product length is the primary reason the limitation on LOQ and is explicitly shown to be an important consideration for primer design for qPCR in general.

Evaluation of a real-time RT-PCR panel for detection of SARS-CoV-2 in bat guano

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19), which is an ongoing global health concern. The exact source of the virus has not been identified, but it is believed that this novel coronavirus originated in animals; bats in particular have been implicated as the primary reservoir of the virus. SARS-CoV-2 can also be transmitted from humans to other animals, including tigers, cats, and mink. Consequently, infected people who work directly with bats could transfer the virus to a wild North American bat, resulting in a new natural reservoir for the virus, and lead to new outbreaks of human disease. We evaluated a reverse-transcription real-time PCR panel for detection of SARS-CoV-2 in bat guano. We found the panel to be highly specific for SARS-CoV-2, and able to detect the virus in bat guano samples spiked with SARS-CoV-2 viral RNA. Our panel could be utilized by wildlife agencies to test bats in rehabilitation facilities prior to their release to the wild, minimizing the risk of spreading this virus to wild bat populations.

Analysis of the stability of 70 housekeeping genes during iPS reprogramming

Studies on induced pluripotent stem (iPS) cells highly rely on the investigation of their gene expression which requires normalization by housekeeping genes. Whether the housekeeping genes are stable during the iPS reprogramming, a transition of cell state known to be associated with profound changes, has been overlooked. In this study we analyzed the expression patterns of the most comprehensive list to date of housekeeping genes during iPS reprogramming of a mouse neural stem cell line N31. Our results show that housekeeping genes' expression fluctuates significantly during the iPS reprogramming. Clustering analysis shows that ribosomal genes' expression is rising, while the expression of cell-specific genes, such as vimentin (Vim) or elastin (Eln), is decreasing. To ensure the robustness of the obtained data, we performed a correlative analysis of the genes. Overall, all 70 genes analyzed changed the expression more than two-fold during the reprogramming. The scale of this analysis, that takes into account 70 previously known and newly suggested genes, allowed us to choose the most stable of all genes. We highlight the fact of fluctuation of housekeeping genes during iPS reprogramming, and propose that, to ensure robustness of qPCR experiments in iPS cells, housekeeping genes should be used together in combination, and with a prior testing in a specific line used in each study. We suggest that the longest splice variants of Rpl13a, Rplp1 and Rps18 can be used as a starting point for such initial testing as the most stable candidates.

Critique of the pairwise method for estimating qPCR amplification efficiency: beware of correlated data!

Background

A recently proposed method for estimating qPCR amplification efficiency E analyzes fluorescence intensity ratios from pairs of points deemed to lie in the exponential growth region on the amplification curves for all reactions in a dilution series. This method suffers from a serious problem: The resulting ratios are highly correlated, as they involve multiple use of the raw data, for example, yielding ~ 250 E estimates from ~ 25 intensity readings. The resulting statistics for such estimates are falsely optimistic in their assessment of the estimation precision.

Results

Monte Carlo simulations confirm that the correlated pairs method yields precision estimates that are better than actual by a factor of two or more. This result is further supported by estimating E by both pairwise and Cq calibration methods for the 16 replicate datasets from the critiqued work, and then comparing the ensemble statistics for these methods.

Conclusion

Contrary to assertions in the proposing work, the pairwise method does not yield E estimates a factor of 2 more precise than estimates from Cq calibration fitting (the standard curve method). On the other hand, the statistically correct direct fit of the data to the model behind the pairwise method can yield E estimates of comparable precision. Ways in which the approach might be improved are discussed briefly.